The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects
Traffic flow prediction (TFP) is an important topic in the fields of operation research and traffic engineering. It is dedicated to predicting the flow of people and vehicles in the transportation network within a specific time frame in the future. Accurate TFP has great significance for traffic man...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01010.pdf |
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| author | Li Minghui Zhou Decheng Zhang Shiqi |
| author_facet | Li Minghui Zhou Decheng Zhang Shiqi |
| author_sort | Li Minghui |
| collection | DOAJ |
| description | Traffic flow prediction (TFP) is an important topic in the fields of operation research and traffic engineering. It is dedicated to predicting the flow of people and vehicles in the transportation network within a specific time frame in the future. Accurate TFP has great significance for traffic management, urban planning, road design, and the development of intelligent transportation systems (ITS). This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. How to fully extract the spatiotemporal correlation of traffic flow (SCTF) is an urgent problem that needs to be solved based on DL prediction models. Concurrently, as science and technology advance, a growing variety of academics are attempting to incorporate reinforcement learning (RL) into TFP. Experimental results show that it can reduce vehicle queuing time and average delay to a greater extent, and alleviate air pollution. The article summarizes the models of DL and RL in TFP, comprehensively compares the benefits and drawbacks of various approaches, and proposes a vision for existing problems and future development. |
| format | Article |
| id | doaj-art-34812a95e0064d7fb103588be025983c |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-34812a95e0064d7fb103588be025983c2025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700101010.1051/itmconf/20257001010itmconf_dai2024_01010The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and ProspectsLi Minghui0Zhou Decheng1Zhang Shiqi2SWUFE-UD Institute of Data Science at SWUFE, Southwestern University of Finance and EconomicsSchool of Information Science and Technology, ShanghaiTech UniversityDepartment of Civil and Environmental Engineering, University of MichiganTraffic flow prediction (TFP) is an important topic in the fields of operation research and traffic engineering. It is dedicated to predicting the flow of people and vehicles in the transportation network within a specific time frame in the future. Accurate TFP has great significance for traffic management, urban planning, road design, and the development of intelligent transportation systems (ITS). This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. How to fully extract the spatiotemporal correlation of traffic flow (SCTF) is an urgent problem that needs to be solved based on DL prediction models. Concurrently, as science and technology advance, a growing variety of academics are attempting to incorporate reinforcement learning (RL) into TFP. Experimental results show that it can reduce vehicle queuing time and average delay to a greater extent, and alleviate air pollution. The article summarizes the models of DL and RL in TFP, comprehensively compares the benefits and drawbacks of various approaches, and proposes a vision for existing problems and future development.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01010.pdf |
| spellingShingle | Li Minghui Zhou Decheng Zhang Shiqi The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects ITM Web of Conferences |
| title | The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects |
| title_full | The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects |
| title_fullStr | The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects |
| title_full_unstemmed | The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects |
| title_short | The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects |
| title_sort | application of reinforcement learning in traffic flow prediction advantages problems and prospects |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01010.pdf |
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